Next Article in Journal
Healthcare-Acquired Infection Surveillance in Neurosurgery Patients, Incidence and Microbiology, Five Years of Experience in Two Polish Units
Previous Article in Journal
Factors Associated with Colorectal Polyps in Middle-Aged and Elderly Populations
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
IJERPH
Volume 19
Issue 12
10.3390/ijerph19127542
ijerph-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles thumb_up
...
Endorse textsms
...
Comment
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Identifying the Psychometric Properties of the Malay Version of the WHOQOL-BREF among Employees with Obesity Problem

by
Mohd Helma Rusyda
1,*,
Nor Ba’yah Abdul Kadir
1,
Wan Nur Khairunnisa Ismail
1,
Siti Jamiaah Abdul Jalil
2,
Nurul-Azza Abdullah
1,
Arena Che Kasim
1,
Suzana Mohd. Hoesni
1 and
Mohd Rizal Abdul Manaf
3
1
Centre for Research in Psychology and Human Well-being, Faculty of Social Sciences and Humanities, Universiti Kebangsaan Malaysia, Bangi 43600, Malaysia
2
Department of Dakwah and Leadership, Faculty of Islamic Studies, Universiti Kebangsan Malaysia, Bangi 43600, Malaysia
3
Department of Community Health, Faculty of Medicine, Universiti Kebangsaan Malaysia, Bangi 43600, Malaysia
*
Author to whom correspondence should be addressed.
Int. J. Environ. Res. Public Health 2022, 19(12), 7542; https://doi.org/10.3390/ijerph19127542
Submission received: 23 May 2022 / Revised: 9 June 2022 / Accepted: 17 June 2022 / Published: 20 June 2022
(This article belongs to the Section Health-Related Quality of Life and Well-Being)
Browse Figure
Review Reports Versions Notes

Abstract

:
The Malay version of the WHOQOL-BREF was published approximately 15 years ago. Since then, no known research has been conducted to identify the psychometric properties of the scale using confirmatory factor analysis. This study aimed to establish a model by applying a scientific approach to the translation and adaptation method. The back translation technique was used for the translation process. This cross-sectional study involved 282 employees at Universiti Kebangsaan Malaysia. The instrument received satisfactory Cronbach’s alpha reliability values. The data were analysed with SEM using AMOS. Results showed that the model produced is parsimonious, with CMIN/df = 0.23, CFI = 0.93, SRMR = 0.08, RMSEA = 0.08 and PCLOSE = 0.07. Adopting the Malay version of the WHOQOL-BREF for future research is highly recommended due to its properties.

1. Introduction

The assessment of quality of life in relation to health has been well established worldwide [1,2,3]. The World Health Organization Quality of Life (WHOQOL) assessment is the most widely used self-reporting measure for examining the quality of life among the general population [1,4], patients [5,6], students [7,8], community [9,10] and employees [11,12]. The WHO defines quality of life as “an individual’s perception of their position in life in the context of the culture and value systems in which they live and in relation to their goals, expectations, standards and concerns” [13]. This instrument consists of 24 facets of QOL, each with 4 items, and 4 additional items relating to the “overall quality of life and general health”. The 24-item WHOQOL-BREF has been developed as a short version of the WHOQOL-100.
According to reports, the WHOQOL can be used across cultures [14,15,16] and has satisfactory psychometric properties. The short version, the WHOQOL-BREF, has been applied particularly in clinical settings and large-scale epidemiological studies. According to a systematic review of the research into quality of life in medicine and health sciences, more than 150 studies have used the WHOQOL-BREF and demonstrated acceptable psychometric results [17]. A bibliometric analysis from 2000–2019 showed that the main contributions to studies into quality of life came from North America and Europe, with fewer research from regions such as Asia, South America and Africa [18].
The earliest report on the psychometric properties of the Malay version of the WHOQOL-BREF was published in 2003 [19]. For the study sample, 200 participants were recruited from the physicians and psychiatric clinics at the Universiti Sains Malaysia Hospital. The sample consisted of 40 healthy participants and 160 patients with hypertension, diabetes mellitus, epilepsy or schizophrenia. The statistical analysis methods employed to report on the psychometric properties comprised the internal consistency, test–retest reliability, concurrent validity, criterion validity and discriminant validity methods. An exploratory factor analysis was used to assess the construct validity of the WHOQOL-BREF. A study [20] using a sample of disabled students found satisfactory Cronbach’s alpha values for the domains of the WHOQOL-BREF covering physical health (0.72), psychological health (0.82) and environmental health (0.80), whereas social relationships received a value of 0.69. The factor analysis found that the social relationship items had poor factor loadings (<0.40), indicating that some items were unsuitable for measuring the social relationships of disabled students. Similar findings were reported in a sample of disabled individuals in Malaysia [21].
To date, although the Malay version of the WHOQOL has been used in a Malaysian setting for 15 years, no studies have used a confirmatory factor analysis to validate it. Therefore, this study collected recent data from employees with obesity in Malaysia to assess the psychometric properties of the Malay version of the WHOQOL-BREF using a confirmatory factor analysis (CFA). The primary aim was to examine the construct validity and reliability of the Malay version of the WHOQOL-BREF. The study addressed the following research question: does the four-factor model of the WHOQOL-BREF show a satisfactory construct validity in terms of dimensionality, convergent and discriminant validity, and reliability (internal consistency, floor-ceiling) in university employees with obesity?

2. Materials and Methods

2.1. Participants

This study, which used a cross-sectional research design, included 282 participants (198 females, 84 males), who were employed at Universiti Kebangsaan Malaysia in Bangi, Malaysia. The participants varied in terms of their designation and level of employment and were defined as obese, as their Body Mass Index (BMI) was >25. The participants completed a self-reporting questionnaire via Google Forms.

2.2. Translation and Cultural Adaptation Methods

It is very important to choose the most appropriate approach and method for translation and adaptation [22]. This research employed the linguistic approach to translation and selected back-translation as the adaptation method. Linguistic translation is a grammar-focused translation, which provides an equivalent meaning between the source language (SL) and target language (TL) with similar psychometric properties, in relation to cultural, social and political contexts [23,24,25]. Compared to other translation approaches, linguistic translation provides equivalence, which may be impossible to achieve with a literal translation [26]. Equivalence occurs when two instruments that are assessing the same construct are compared and are confirmed to be valid [27,28,29,30], free from cultural bias, and are acceptable and relevant in the culture [31,32]. To achieve instrument equivalence, a researcher must choose the most appropriate method for translation. Therefore, back-translation was chosen for this study as it can produce equivalence for the research instruments, and consequently, for the research findings [33,34,35]. Equivalence is assessed through a detailed examination of the accuracy of an instrument produced by multiple translators and translations (extra checks) [36]. Back-translation is a three-step process, which involves translating to the TL, translating back to the SL, and comparing the versions [37]. The instrument must first be translated from the SL (English) into the TL (Malay language) by a bilingual translator. Next, a blind translator must translate the instrument back into the SL. All incongruities must be discussed between the translators [38,39,40]. To ensure the accuracy of the instrument, the translators were selected and the translation was conducted according to the guidelines [41] provided by Brislin. These guidelines include a formal qualification as a translator into the TL, first-language experience in the TL, excellent knowledge of English, experience of living and working in an English-language environment, and familiarity with the culture associated with the TL. Brislin originally recommended a 7-step process for back-translation [42].
  • Step 1
In this step, a document or measuring tool is translated to a TL. Researchers can translate a measuring tool if they are native speakers of the TL [43]. However, they must recognise that their formal education may influence the translation as they may have a propensity to use academic terms that are not well understood by potential participants [44]. Additionally, the researchers and participants may have a different understanding of the terms, and this will affect the congruency of the translation [45]. For this study, it was decided to translate the instrument into the Malay language (TL), and to then send the translated material to another translator. This will be discussed in Step 2.
  • Step 2
To overcome problems that may arise in Step 1, the translated instrument is sent to another translator, designated as Translator A (TA). In this step, the TA must translate the instrument back into the SL. Furthermore, a second translator (Translator B; TB) is given the original version in the SL to be translated to the TL. The main criteria for choosing the translators for this process are their qualifications and expertise in both languages [46]. Ideally, the translators should have experience in the SL and TL (English and Malay language). It should be noted that the translators for Step 2 should not have knowledge in the research field or subject because it can affect the meanings and terms [47]. On completion, the translated version in the SL (from TA) is submitted to the next translator.
  • Step 3
In this step, a translator must be knowledgeable in the SL and TL. Ideally, the translator should be qualified in the research field and the instrument [48]. Furthermore, it is highly recommended that the translator should have a high academic qualification, such as a master’s degree or doctoral candidate status [49].
  • Step 4
Next, discrepancies between versions of the instrument are discussed by translators. All the translated instruments and the original version in the SL are brought together and compared. In this step, the versions must be modified until the researcher and translators agree with the translation [50]. This stage is critical and time-consuming. Translators are encouraged to maintain the meanings despite having to make some minor changes during deliberations [51].
  • Step 5
After agreement has been reached on the translation result, the translated instrument can be distributed to potential respondents for pilot testing [52]. Although the number of respondents required for qualitative research is not specified, 10 respondents are typically regarded as adequate for a pilot test [53]. For quantitative research, at least 100 respondents are required to attain reliability and validity.
  • Step 6
In this step, all the data are gathered and analysed by the researcher. If the proposed model is shown to fit, then it can be said that the instrument demonstrates reliability and validity [53].
  • Step 7
If the researcher is satisfied with the findings, the instrument is used on the targeted population.

2.3. Measure

The 24-item WHOQOL-BREF is a self-reporting measurement that covers four areas: physical health (7 items), psychological health (6 items), social relationships (3 items) and environmental health (8 items). Participants respond to the items on a five-point Likert-type scale. The physical health domain includes items on mobility, daily activities, functional capacity, energy, pain and sleep. The psychological domain items include self-image, negative thoughts, positive attitudes, self-esteem, mentality, learning ability, memory concentration, religion and mental status. The social relationships domain contains questions on personal relationships, social support and sex life. The environmental health domain covers issues related to financial resources, safety, health and social services, physical living environment, opportunities to acquire new skills and knowledge, recreation, general environment (noise, air pollution, etc.) and transportation. The report showed that the WHOQOL-BREF has good psychometric properties.

2.4. Statistical Analysis

The participants completed self-reporting measurements via Google Forms. The responses were analysed with structural equation modelling-AMOS (SEM-AMOS) to identify the relationships between the variables and determine the fit of the model. Although there are no guidelines specifying the optimal sample size for a factor analysis, the larger the sample size, the smaller the standard error [54,55]. Nevertheless, it is important to note that the aim of this study was to test the theoretical model, not the population model; therefore, a sample size of N ≥ 200 was deemed to be sufficient [56,57,58].
A confirmatory factor analysis (CFA), which is a form of factor analysis, is also a theory-driven analysis [59]. It can measure convergent and discriminant validity [60] and confirm a hypothesis by demonstrating an established relationship between observed variables and their underlying latent constructs [61]. An SEM-AMOS analysis was used in this study because of its ability to test a hypothesis based on a theoretically-specified model [62].

3. Results

To determine whether the sample size was sufficient, a KMO test was employed to examine the sampling adequacy for a factor analysis. The KMO measure of sampling adequacy standards are as follows: 0.00 to 0.49 (unacceptable), 0.50 to 0.59 (miserable), 0.60 to 0.69 (mediocre), 0.70 to 0.79 (middling), 0.80 to 0.89 (meritorious) and 0.90 to 1 (marvellous) [63]. A value between 0.8 and 1 reflects the best cut-off point and fit for a factor analysis [64]. A KMO value of 0.89 was obtained for this analysis, thereby deeming it as “meritorious” [65], whereas the Cronbach’s alpha value was 0.88. Table 1 shows the correlations between the items. All the items were retained since none of them were highly correlated.

3.1. Factor Loading and Communalities

Although a factor loading of 0.30 was acceptable [66], other aspects, such as communality, had to be considered. Communality is the shared variance reflected by the sum of the squared factor loading [67]. The factors with the lowest communality values were identified based on the data shown in Table 2. As suggested, any communality below 0.20 was eliminated.
Table 3 presented the vaidity findings of this paper. The convergent validity of each factor is estimated based on an average shared variance (AVE) > 0.5 [67]. AVE is the average amount of variance in observed variables that a latent construct can explain [68]. The discriminant validity emphasises the items’ capacity to be distinct from other factors other than their parent factor [68]. The maximum shared squared variance (MSV) is the benchmark for discriminant validity [69]. A factor is distinct if the MSV value is smaller than the AVE [70].
For reliability, most researchers determine Cronbach’s alpha readings as 0.70 and above [71]. However, Cronbach’s alpha value is the most basic reliability testing and can be used if a model has only one factor [72]. The most common measurements for reliability are composite reliability (CR), and maximal reliability (MaxR (H)) because these tests can accurately measure reliability [73]. CR reading is achieved when all the “standardized” items are allowed to correlate with each other (intraclass correlation). The benchmark in evaluating CR is >0.7 and, usually, MaxR (H) has a higher reading than CR [74]. Normality testing findings can be examined in Table 4.

3.2. Model Fit Assessment

For a model fit assessment, the Hu and Bentler [75] threshold or cut-off point, namely, the chi-square to df ratio (χ2/df), comparative fit index (CFI), goodness-of-fit index (GFI), adjusted goodness-of-fit index (AGFI), standardised root mean square residual (SRMR), root mean square error of approximations (RMSEA) and PCLOSE function can be used. However, as recommended, the GFI and AGFI were disregarded in this study as they are highly sensitive to sample size [76]. The cut-off point for model fit indicated in Table 5.
As expected, four domains were produced from the analysis, in line with the WHOQOL-BREF framework. However, the initial finding of the modelling at a CFI of 0.86 was unsatisfactory. Therefore, the factor loadings and communalities were examined, and it was decided to remove all the items with communalities below 0.20. Finally, the default model had an acceptable parsimonious model fit with CMIN/df = 0.23, CFI = 0.93, SRMR = 0.08, RMSEA = 0.08 and PCLOSE at 0.07. The measurement model is presented in Figure 1.

4. Discussion

This study attempted to identify the psychometric properties of the Malay version of the 24-item WHOQOL-BREF. The translation was conducted in accordance with the back-translation protocol. The participants were university employees with obesity, mostly with a BMI of 25 or higher. Although the CFA indicated a good model fit, it was necessary to address a few issues. In addition, inconsistent items were identified and had to be eventually deleted from the model.
The study found four domains of the WHOQOL that were consistent with previous research on the WHOQOL-BREF [77,78,79]. Although some earlier research indicated that, in some cases, the factor extraction of the WHOQOL-BREF could be a one-factor solution [80,81], the four-factor solution was deemed necessary for a better model fit, as the former has disadvantages in terms of inter-item correlations [82,83].
Table 2 shows the communalities of each item in the WHOQOL-BREF domains. In the physical health domain, several items with low communalities were identified. For example, the item, “How much do you need any medical treatment to function in your daily life?”, had the lowest communality of 0.13, thereby suggesting that the item could not be explained by the factor. Although the research sample was university employees with obesity, this result was consistent with previous research on physical health among patients with diabetes mellitus, where it was found that medical treatment was not the most significant predictor in maintaining quality of life as compared to psychological health and social relationships [84,85,86]. Two other items, “To what extent do you feel that physical pain prevents you from doing what you need to do?” and “How well are you able to get around?”, also had low communalities, thereby indicating that these concerns were insignificant for the research participants with obesity and could not be explained by the physical domain factor. This finding was also consistent with that of previous research [12,87]. Therefore, it was decided to exclude these three items with low communalities from the physical domain.
Within the psychological domain, the item, “How often do you have negative feelings such as blue mood, despair, anxiety, depression?”, had the lowest communality. One reason for this may have been the fact that the question was double-barrelled. It might have been difficult for the participants to respond to multiple mental health issues in one item, as double-barrelled questions are open to predisposed answers or misapprehensions [88,89,90]. Furthermore, as suggested by the WHO, any WHOQOL-BREF questions that are double-barrelled should be edited or removed to maintain the validity and reliability [91].
Among eastern societies such as Malaysia, questions about sexual issues are often considered taboo. The item, “How satisfied are you with sex life?”, had a low communality value of 0.18 among all the items. However, this finding was not unexpected, based on the large number of previous research on sexual issues within the same context [92]. For example, a Turkish researcher had to remove an item on sexuality and personal life due to the low factor loading, eventually producing a model that did not fit the population. In a study on the Iranian population, the Cronbach’s alpha was improved by removing items on sexual activities [93]. Similar results were demonstrated by studies on sexual issues within a Malaysian setting. For instance, items on sexual intentions and premarital sex among Malaysian youths were removed due to a very low factor loading [94] or weak factor correlation [95].
In the environmental domain, the item, “To what extent do you have the opportunity for leisure activities?”, had the lowest communality value due to the daily work demands of Malaysian employees [96,97], leading to the feasible argument that the participants may have little opportunity to participate in leisure activities. Other than this result, the research findings supported those of previous research in the environmental domain and quality of life, which suggested that the environmental domain is strongly influenced by a quality environment [98,99]. Furthermore, this finding was consistent with previous research that indicated that compared to the other items, this item is a weak predictor of quality of life [100,101].

5. Implications

A systematic, scientific translation protocol and statistical analysis showed that the Malay version of the WHOQOL-BREF has excellent psychometric properties. Furthermore, the model that was produced was deemed to be parsimonious. Therefore, it is recommended that this version by adopted for any future research. However, it is proposed that factors with communality values of less than 0.20 be eliminated to avoid any issues during the modelling process. It is highly recommended that any future research that wishes to use the original items should refine all the items to ensure better inter-item correlations.

6. Conclusions

The study results are crucial to confirm whether the four-factor solution model can be replicated in the Malaysian setting. Although some of the items were removed, the model fit was satisfactory, and the psychometric properties were presented.

Author Contributions

Conceptualization, N.B.A.K. and M.H.R.; methodology, M.H.R., N.B.A.K. and M.R.A.M.; validation, M.H.R., M.R.A.M. and W.N.K.I.; formal analysis, M.H.R., N.B.A.K. and W.N.K.I.; investigation, S.M.H., A.C.K. and S.J.A.J.; resources, N.B.A.K. and M.R.A.M.; writing—original draft preparation, M.H.R.; writing—review and editing, M.R.A.M., M.H.R., N.-A.A., S.M.H., S.J.A.J. and A.C.K.; supervision, N.B.A.K., S.M.H. and M.H.R. All authors have read and agreed to the published version of the manuscript.

Funding

Centre of IDEA-UKM, Universiti Kebangsaan Malaysia, grant number DCP-2018-005/2; “The APC was funded by Universiti Kebangsaan Malaysia”.

Institutional Review Board Statement

The study was conducted in accordance with the Declaration of Helsinki, and approved by the Institutional Review Board (or Ethics Committee) of UNIVERSITI KEBANGSAAN MALAYSIA (UKM PPI.800-1/1/5/JEP-2019-391 and date of approval on 17 June 2019).

Informed Consent Statement

Informed consent was obtained from all subjects involved in the study.

Data Availability Statement

The data presented in this study are available within the article.

Acknowledgments

We thank all participants for their commitments and contribution to this study.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Lodhi, F.S.; Montazeri, A.; Nedjat, S.; Mahmoodi, M.; Farooq, U.; Yaseri, M.; Holakouie-Naieni, K. Assessing the quality of life among Pakistani general population and their associated factors by using the World Health Organization’s quality of life instrument (WHOQOL-BREF): A population based cross-sectional study. Health Qual. Life Outcomes 2019, 17, 1–17. [Google Scholar] [CrossRef] [Green Version]
  2. Carrard, V.; Kunz, S.; Peter, C. Mental health, quality of life, self-efficacy, and social support of individuals living with spinal cord injury in Switzerland compared to that of the general population. Spinal Cord. 2021, 59, 398–409. [Google Scholar] [CrossRef] [PubMed]
  3. Epifanio, M.S.; Andrei, F.; Mancini, G.; Agostini, F.; Piombo, M.A.; Spicuzza, V.; La Grutta, S. The impact of COVID-19 pandemic and lockdown measures on quality of life among Italian general population. J. Clin. Med. 2021, 10, 289. [Google Scholar] [CrossRef] [PubMed]
  4. Balchin, C.; Tan, A.L.; Wilson, O.J.; McKenna, J.; Stavropoulos-Kalinoglou, A. Participation in physical activity decreased more in people with rheumatoid arthritis than the general population during the COVID-19 lockdown: A cross-sectional study. Rheumatol. Int. 2022, 42, 241–250. [Google Scholar] [CrossRef] [PubMed]
  5. Al-Taie, N.; Maftei, D.; Kautzky-Willer, A.; Krebs, M.; Stingl, H. Assessing the quality of life among patients with diabetes in Austria and the correlation between glycemic control and the quality of life. Prim. Care Diabetes 2020, 14, 133–138. [Google Scholar] [CrossRef] [PubMed]
  6. Iqbal, M.S.; Kassab, Y.W.; Al-Saikhan, F.I.; Almalki, Z.S.; Haseeb, A.; Iqbal, M.Z.; Ali, M. Assessing quality of life using WHOQOL-BREF: A cross-sectional insight among patients on warfarin in Malaysia. Saudi Pharm. J. 2020, 28, 936–942. [Google Scholar] [CrossRef] [PubMed]
  7. Ilić, I.; Šipetić-Grujičić, S.; Grujičić, J.; Živanović Mačužić, I.; Kocić, S.; Ilić, M. Psychometric properties of the world health organization’s quality of life (WHOQOL-BREF) questionnaire in medical students. Medicina 2019, 55, 772. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  8. Roy, B.; Naidu, C.R.; Perumal, V.; Vallipuram, P.; Subramaniam, J.; Chandrakeran, S.K.; Jacob, L. Quality of life (QoL) among medical students of clinical years in Perak state, Malaysia: A study using the WHOQOL-BREF instrument. Quest Int. J. Med. Health Sci. 2020, 3, 24–32. [Google Scholar]
  9. Gagliardi, J.; Brettschneider, C.; König, H.H. Health-related quality of life of refugees: A systematic review of studies using the WHOQOL-Bref instrument in general and clinical refugee populations in the community setting. Confl. Health 2021, 15, 1–14. [Google Scholar] [CrossRef]
  10. Yee, A.; Hodori, N.A.M.; Tung, Y.Z.; Ooi, P.L.; Latif, S.A.B.A.; Isa, H.M.; Tan, S.B. Depression level and coping responses toward the movement control order and its impact on quality of life in the Malaysian community during the COVID-19 pandemic: A web-based cross-sectional study. Ann. Gen. Psychiatry 2021, 20, 1–9. [Google Scholar] [CrossRef]
  11. Korkmaz, S.; Kazgan, A.; Çekiç, S.; Tartar, A.S.; Balcı, H.N.; Atmaca, M. The anxiety levels, quality of sleep and life and problem-solving skills in healthcare workers employed in COVID-19 services. J. Clin. Neurosci. 2021, 80, 131–136. [Google Scholar] [CrossRef]
  12. Abdul Manaf, M.R.; Mohammed Nawi, A.; Mohd Tauhid, N.; Othman, H.; Abdul Rahman, M.R.; Mohd Yusoff, H.; Ganasegeran, K. Factors Associated with Health-Related Quality of Life amongst Employees in a Malaysian Public University. Int. J. Environ. Res. Public Health 2021, 18, 10903. [Google Scholar] [CrossRef]
  13. The WHOQOL Group. WHOQOL-BREF Introduction, Administration and Scoring, Field Trial Version; World Health Organization: Geneva, Switzerland, 1996. [Google Scholar]
  14. Lucchetti, G.; Damiano, R.F.; DiLalla, L.F.; Lucchetti, A.L.G.; Moutinho, I.L.D.; da Silva Ezequiel, O.; Kevin Dorsey, J. Cross-cultural differences in mental health, quality of life, empathy, and burnout between US and Brazilian medical students. Acad. Psychiatry 2018, 42, 62–67. [Google Scholar] [CrossRef]
  15. Rezaeipandari, H.; Morowatisharifabad, M.A.; Mohammadpoorasl, A.; Shaghaghi, A. Cross-cultural adaptation and psychometric validation of the World Health Organization quality of life-old module (WHOQOL-OLD) for Persian-speaking populations. Health Qual. Life Outcomes 2020, 18, 1–7. [Google Scholar] [CrossRef] [Green Version]
  16. Saxena, S.; Carlson, D.; Billington, R.; Orley, J. The WHO quality of life assessment instrument (WHOQOL-Bref): The importance of its items for cross-cultural research. Qual. Life Res. 2001, 10, 711–721. [Google Scholar] [CrossRef]
  17. Haraldstad, K.; Wahl, A.; Andenæs, R.; Andersen, J.R.; Andersen, M.H.; Beisland, E.; Helseth, S. A systematic review of quality of life research in medicine and health sciences. Qual. Life Res. 2019, 28, 2641–2650. [Google Scholar] [CrossRef] [Green Version]
  18. Zheng, S.; He, A.; Yu, Y.; Jiang, L.; Liang, J.; Wang, P. Research trends and hotspots of health-related quality of life: A bibliometric analysis from 2000 to 2019. Health Qual. Life Outcomes 2021, 19, 1–13. [Google Scholar] [CrossRef]
  19. Hasanah, C.I.; Naing, L.; Rahman, A.R.A. World Health Organization quality of life assessment: Brief version in Bahasa Malaysia. Med. J. Malays. 2003, 58, 79–88. [Google Scholar]
  20. Abdullah Bandar, N.F.; Jani, R.; Karim, M.A. Psychometric properties of the WHOQOL-BREF questionnaire among disabled students in Malaysian higher learning institutions. Appl. Res. Qual. Life 2014, 9, 469–478. [Google Scholar] [CrossRef]
  21. Jani, R.; Alias, A.A.; Awang, H.; Arunasalam, R.S. Psychometric properties of the WHO quality of life disability (WHOQOL-DIS) among persons with disabilities in Malaysia. Pertanika. J. Soc. Sci. Humanit. 2020, 28, 2113–2127. [Google Scholar]
  22. Hambleton, R.K.; Li, S. Translation and Adaptation Issues and Methods for Educational and Psychological Tests. In Comprehensive Handbook of Multicultural School Psychology; Frisby, C.L., Reynolds, C.R., Eds.; John Wiley Sons, Inc.: Hoboken, NJ, USA, 2005; pp. 881–903. [Google Scholar]
  23. Prieto, A.J. A Method for Translation of Instruments to Other Languages. Adult Educ. Q. 1992, 43, 1–14. [Google Scholar] [CrossRef]
  24. Vermeer, H.J. Describing nonverbal behavior in the Odyssey: Scenes and verbal frames as translation problems. In Advances in Nonverbal Communication: Sociocultural, Clinical, Esthetic and Literary Per-Spectives; Poyatos, F., Ed.; John Benjamins Publishing: Amsterdam, The Netherlands, 1992; pp. 285–300. [Google Scholar]
  25. Gudmundsson, E. Guidelines for translating and adapting psychological instruments. Nord. Psychol. 2009, 61, 29–45. [Google Scholar] [CrossRef]
  26. Ferrer, M.; Alonso, J.; Prieto, L.; Plaza, V.; Monsó, E.; Marrades, R.; Antó, J.M. Validity and reliability of the St George’s Respiratory Questionnaire after adaptation to a different language and culture: The Spanish example. Eur. Respir. J. 1996, 9, 1160–1166. [Google Scholar] [CrossRef] [Green Version]
  27. Chang, A.M.; Chau, J.P.; Holroyd, E. Translation of questionnaires and issues of equivalence. J. Adv. Nurs. 1999, 29, 316–322. [Google Scholar] [CrossRef]
  28. Edwards, J. Minority Languages and Group Identity: Cases and Categories; John Benjamins Publishing: Amsterdam, The Netherlands, 2010; Volume 27. [Google Scholar]
  29. Harkness, J.; Schoua-Glusberg, A. Questionnaires in translation. In Cross-Cultural Survey Equivalence; Harkness, J., Ed.; Zentrum für Umfragen, Methoden und Analysen ZUMA: Mannheim, Germany, 1998; pp. 87–126. [Google Scholar]
  30. Van de Vijver, F.; Tanzer, N.K. Bias and equivalence in cross-cultural assessment: An overview. Eur. Rev. Appl. Psychology 2004, 54, 119–135. [Google Scholar] [CrossRef] [Green Version]
  31. Douglas, S.P.; Craig, C.S. Collaborative and iterative translation: An alternative approach to back translation. J. Int. Mark. 2007, 15, 30–43. [Google Scholar] [CrossRef]
  32. Wu, L.F.; Yang, S.H.; Koo, M. Psychometric properties of the Chinese version of Spiritual Index of Well-Being in elderly Taiwanese. BMC Geriatr. 2017, 17, 3. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  33. Brislin, R.W. Back-translation for cross-cultural research. J. Cross-Cult. Psychol. 1970, 1, 185–216. [Google Scholar] [CrossRef]
  34. Del Greco, L.; Walop, W.; Eastridge, L. Questionnaire development: 3. Translation. CMAJ Can. Med. Assoc. J. 1987, 136, 817. [Google Scholar]
  35. Triandis, H.C.; Brislin, R.W. Cross-cultural psychology. Am. Psychol. 1984, 39, 1006. [Google Scholar] [CrossRef]
  36. Saleem, S.; Larimo, J.A.; Ummik, K.; Kuusik, A. Cultural and paradoxical values in advertising in Eastern Europe: Evidence from Estonia. Balt. J. Manag. 2015, 10, 313–330. [Google Scholar] [CrossRef]
  37. Maxwell, B. Translation and cultural adaptation of the survey instruments. In Third International Mathematics and Science Study (TIMSS) Technical Report; Martin, M.O., Kelly, D.L., Eds.; Boston College: Chestnut Hill, MA, USA, 1996; Volume 1, pp. 159–169. [Google Scholar]
  38. Brislin, R.W. The wording and translation of research instruments. In Field Methods in Cross-Cultural Research; Lonner, W.J., Berry, J.W., Eds.; SAGE Publications: Beverly Hills, CA, USA, 1986; pp. 137–164. [Google Scholar]
  39. Jones, P.S.; Lee, J.W.; Phillips, L.R.; Zhang, X.E.; Jaceldo, K.B. An adaptation of Brislin’s translation model for cross-cultural research. Nurs. Res. 2001, 50, 300–304. [Google Scholar] [CrossRef] [PubMed]
  40. Tsai, C. Inside the television newsroom: An insider’s view of international news translation in Taiwan. Lang. Intercult. Commun. 2005, 5, 145–153. [Google Scholar] [CrossRef]
  41. Mezias, J.M.; Starbuck, W.H. Studying the accuracy of managers’ perceptions: A research odyssey. Br. J. Manag. 2003, 14, 3–17. [Google Scholar] [CrossRef]
  42. Tweney, R.D.; Hoemann, H.W.; Andrews, C.E. Semantic organization in deaf and hearing subjects. J. Psycholinguist. Res. 1975, 4, 61–73. [Google Scholar] [CrossRef]
  43. Bracken, B.A.; Barona, A. State of the art procedures for translating, validating and using psychoeducational tests in cross-cultural assessment. Sch. Psychol. Int. 1991, 12, 119–132. [Google Scholar] [CrossRef]
  44. Maneesriwongul, W.; Dixon, J.K. Instrument translation process: A methods review. J. Adv. Nurs. 2004, 48, 175–186. [Google Scholar] [CrossRef]
  45. Sousa, V.D.; Rojjanasrirat, W. Translation, adaptation and validation of instruments or scales for use in cross-cultural health care research: A clear and user-friendly guideline. J. Eval. Clin. Pract. 2011, 17, 268–274. [Google Scholar] [CrossRef]
  46. Squires, A. Methodological challenges in cross-language qualitative research: A research review. Int. J. Nurs. Stud. 2009, 46, 277–287. [Google Scholar] [CrossRef] [Green Version]
  47. Baumbusch, J.L.; Kirkham, S.R.; Khan, K.B.; McDonald, H.; Semeniuk, P.; Tan, E.; Anderson, J.M. Pursuing common agendas: A collaborative model for knowledge translation between research and practice in clinical settings. Res. Nurs. Health 2008, 31, 130–140. [Google Scholar] [CrossRef]
  48. Zhao, H.; Kanda, K. Translation and validation of the standard Chinese version of the EORTC QLQ-C30. Qual. Life Res. 2000, 9, 129–137. [Google Scholar] [CrossRef]
  49. Formea, C.M.; Mohamed, A.A.; Hassan, A.; Osman, A.; Weis, J.A.; Sia, I.G.; Wieland, M.L. Lessons learned: Cultural and linguistic enhancement of surveys through community-based participatory research. Prog. Community Health Partnersh. Res. Educ. Action 2014, 8, 331. [Google Scholar] [CrossRef] [Green Version]
  50. McCrae, R.R.; Costa, P.T., Jr. Personality trait structure as a human universal. Am. Psychol. 1997, 52, 509. [Google Scholar] [CrossRef]
  51. Chapman, D.W.; Carter, J.F. Translation procedures for the cross cultural use of measurement instruments. Educ. Eval. Policy Anal. 1979, 1, 71–76. [Google Scholar] [CrossRef]
  52. Squires, J.E.; Ehrenberg, A.; Estabrooks, C.A.; Eldh, A.C.; Wallin, L. Translating and testing the Alberta context tool for use among nurses in Swedish elder care. BMC Health Serv. Res. 2013, 13, 68. [Google Scholar]
  53. Zolotor, A.J.; Runyan, D.K.; Dunne, M.P.; Jain, D.; Péturs, H.R.; Ramirez, C.; Isaeva, O. ISPCAN Child Abuse Screening Tool Children’s Version (ICAST-C): Instrument development and multi-national pilot testing. Child Abus. Negl. 2009, 33, 833–841. [Google Scholar] [CrossRef]
  54. Park, E.H.; So, Y.K.; Choi, N.K.; Kim, S.J.; Noh, J.S.; Ko, Y.J.; Kim, Y.S. The reliability and validity of Korean Conners Parent and Teacher Rating scale. J. Korean Acad. Child Adolesc. Psychiatry 2003, 14, 183–196. [Google Scholar]
  55. Kline, R.B. Structural Equation Modeling; Guilford: New York, NY, USA, 1998. [Google Scholar]
  56. Williams, B.; Onsman, A.; Brown, T. Exploratory factor analysis: A five-step guide for novices. Australas. J. Paramed. 2010, 8, 1–13. [Google Scholar] [CrossRef] [Green Version]
  57. Barrett, P. Structural equation modelling: Adjudging model fit. Personal. Individ. Differ. 2007, 42, 815–824. [Google Scholar] [CrossRef]
  58. Fabrigar, L.R.; Wegener, D.T.; MacCallum, R.C.; Strahan, E.J. Evaluating the use of exploratory factor analysis in psychological research. Psychol. Methods 1999, 4, 272. [Google Scholar] [CrossRef]
  59. MacCallum, R.C.; Widaman, K.F.; Zhang, S.; Hong, S. Sample size in factor analysis. Psychol. Methods 1999, 4, 84. [Google Scholar] [CrossRef]
  60. Peiper, N.; Clayton, R.; Wilson, R.; Illback, R. The performance of the K6 Scale in a large school sample. Psychol. Assess. 2015, 27, 228. [Google Scholar] [CrossRef]
  61. Farrell, A.M.; Rudd, J.M. Factor Analysis and Discriminant Validity: A Brief Review of Some Practical Issues. In Proceedings of the Australia and New Zealand Marketing Academy Conference (ANZMAC), Melbourne, Australia, 30 November–2 December 2009. [Google Scholar]
  62. Suhr, D. Exploratory or Confirmatory Factor Analysis. SAS Users Group International Conference; SAS Institute, Inc.: Cary, NJ, USA, 2006; pp. 1–17. [Google Scholar]
  63. Chen, F.; Curran, P.J.; Bollen, K.A.; Kirby, J.; Paxton, P. An empirical evaluation of the use of fixed cutoff points in RMSEA test statistic in structural equation models. Sociol. Methods Res. 2008, 36, 462–494. [Google Scholar] [CrossRef] [Green Version]
  64. Kaiser, H.F. An index of factorial simplicity. Psychometrika 1974, 39, 31–36. [Google Scholar] [CrossRef]
  65. Watson, R.; Thompson, D.R. Use of factor analysis in Journal of Advanced Nursing: Literature review. J. Adv. Nurs. 2006, 55, 330–341. [Google Scholar] [CrossRef]
  66. Cattell, R.B. A biometrics invited paper. Factor analysis: An introduction to essentials I. The purpose and underlying models. Biometrics 1965, 21, 190–215. [Google Scholar] [CrossRef]
  67. Hair, J.F.; Black, W.C.; Babin, B.J.; Anderson, R.E. Multivariate Data Analysis, 7th ed.; Prentice Hall: Englewood Cliffs, NJ, USA, 2009. [Google Scholar]
  68. Campbell, D.T.; Fiske, D.W. Convergent and discriminant validation by the multitrait-multimethod matrix. Psychol. Bull. 1959, 56, 81–105. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  69. Mussel, P. Epistemic curiosity and related constructs: Lacking evidence of discriminant validity. Personal. Individ. Differ. 2010, 49, 506–510. [Google Scholar] [CrossRef]
  70. DeVon, H.A.; Block, M.E.; Moyle-Wright, P.; Ernst, D.M.; Hayden, S.J.; Lazzara, D.J.; Kostas-Polston, E. A psychometric toolbox for testing validity and reliability. J. Nurs. Scholarsh. 2007, 39, 155–164. [Google Scholar] [CrossRef]
  71. Peterson, R.A. A Meta-analysis of Cronbach’s Coefficient Alpha. J. Consum. Res. 1994, 21, 381–391. [Google Scholar] [CrossRef]
  72. Schmitt, N. Uses and abuses of Coefficient Alpha. Psychol. Assess. 1996, 8, 350–353. [Google Scholar] [CrossRef]
  73. Raykov, T.; Gabler, S.; Dimitrov, D.M. Maximal reliability and composite reliability: Examining their difference for multicomponent measuring instruments using latent variable modeling. Struct. Equ. Modeling: A Multidiscip. J. 2016, 23, 384–391. [Google Scholar] [CrossRef]
  74. Raykov, T. Estimation of maximal reliability: A note on a covariance structure modelling approach. Br. J. Math. Stat. Psychol. 2004, 57, 21–27. [Google Scholar] [CrossRef]
  75. Child, D. The Essentials of Factor Analysis; A&C Black: Edinburgh, UK, 2006. [Google Scholar]
  76. Hu, L.; Bentler, P. Evaluating model fit. In Structural Equation Modeling. Concepts, Issues, and Applications; Hoyle, R.H., Ed.; Sage: London, UK, 1995; pp. 76–99. [Google Scholar]
  77. Sharma, S.; Mukherjee, S.; Kumar, A.; Dillon, W.R. A simulation study to investigate the use of cutoff values for assessing model fit in covariance structure models. J. Bus. Res. 2005, 58, 935–943. [Google Scholar] [CrossRef]
  78. Li, L.; Young, D.; Xiao, S.; Zhou, X.; Zhou, L. Psychometric properties of the WHO Quality of Life questionnaire (WHOQOL-100) in patients with chronic diseases and their caregivers in China. Bull. World Health Organ. 2004, 82, 493–502. [Google Scholar]
  79. Kalfoss, M.H.; Reidunsdatter, R.J.; Klöckner, C.A.; Nilsen, M. Validation of the WHOQOL-Bref: Psychometric properties and normative data for the Norwegian general population. Health Qual. Life Outcomes 2021, 19, 1–12. [Google Scholar] [CrossRef]
  80. Knekta, E.; Runyon, C.; Eddy, S. One size doesn’t fit all: Using factor analysis to gather validity evidence when using surveys in your research. CBE—Life Sci. Educ. 2019, 18, rm1. [Google Scholar] [CrossRef]
  81. Lima, L.R.D.; Funghetto, S.S.; Volpe, C.R.G.; Santos, W.S.; Funez, M.I.; Stival, M.M. Quality of life and time since diagnosis of diabetes mellitus among the elderly. Rev. Bras. De Geriatr. E Gerontol. 2018, 21, 176–185. [Google Scholar] [CrossRef] [Green Version]
  82. Tchicaya, A.; Lorentz, N.; Demarest, S.; Beissel, J.; Wagner, D.R. Relationship between self-reported weight change, educational status, and health-related quality of life in patients with diabetes in Luxembourg. Health Qual. Life Outcomes 2015, 13, 1–9. [Google Scholar] [CrossRef] [Green Version]
  83. Reid, N.; Mason, J.; Kurdyak, P.; Nisenbaum, R.; de Oliveira, C.; Hwang, S.; Stergiopoulos, V. Evaluating the Impact of a Critical Time Intervention Adaptation on Health Care Utilization among Homeless Adults with Mental Health Needs in a Large Urban Center. Can. J. Psychiatry 2022, 67, 57–66. [Google Scholar] [CrossRef] [PubMed]
  84. Uys, L.R.; Holzemer, W.L.; Chirwa, M.L.; Dlamini, P.S.; Greeff, M.; Kohi, T.W.; Makoae, L.N.; Stewart, A.L.; Mullan, J.; Phetlhu, R.D.; et al. The development and validation of the HIV/AIDS Stigma Instrument-Nurse (HASI-N). AIDS Care 2009, 21, 150–159. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  85. Abdul Kadir, N.B.; Ismail, W.N.K.; Abdullah, N.-A.; Helma, R.; Abdul Jalil, S.J.; Che Kasim, A.; Hoesni, S.M.; Manaf, M.R.A. Criterion Validity and Psychometric Properties of a Malay Version of the Short Multidimensional Inventory Lifestyle Evaluation-Confinement (SMILE-C) in a Sample of University Staff with Weight Problems. Int. J. Environ. Res. Public Health 2021, 18, 10410. [Google Scholar] [CrossRef]
  86. Azam, M.; Aslam, M.; Basharat, J.; Mughal, M.A.; Nadeem, M.S.; Anwar, F. An empirical study on quality of life and related factors of Pakistani breast cancer survivors. Sci. Rep. 2021, 11, 1–11. [Google Scholar] [CrossRef] [PubMed]
  87. Farran, N.; Safieddine, B.R.; Bayram, M.; Abi Hanna, T.; Massouh, J.; AlKhawaja, M.; Darwish, H. Factors affecting MS patients’ health-related quality of life and measurement challenges in Lebanon and the MENA region. Mult. Scler. J. –Exp. Transl. Clin. 2020, 6, 2055217319848467. [Google Scholar] [CrossRef] [PubMed]
  88. Alijanpour, M.; Sadatmahalleh, S.J.; Samaneh, Y.; Mahnaz, B.K.; Majid, Y.A.; Moini, A.; Kazemnejad, A. Influence of Psycho-Sexual Factors on the Quality of Life in Pregnant Women during the COVID-19 Pandemic: A Path Analysis. 2021. Available online: https://www.researchsquare.com/article/rs-45196/v2 (accessed on 22 May 2022).
  89. Dinç Horasan, G.; Tarı Selçuk, K.; Sakarya, S.; Sözmen, K.; Ergör, G.; Yardım, N.; Ünal, B. Health-related quality of life and perceived health status of Turkish population. Qual. Life Res. 2019, 28, 2099–2109. [Google Scholar] [CrossRef] [PubMed]
  90. Maasoumi, R.; Mokarami, H.; Nazifi, M.; Stallones, L.; Taban, A.; Yazdani Aval, M.; Samimi, K. Psychometric properties of the Persian translation of the sexual quality of life–male questionnaire. Am. J. Men’s Health 2017, 11, 564–572. [Google Scholar] [CrossRef] [PubMed]
  91. Muhammad, N.A.; Shamsuddin, K.; Amin, R.M.; Omar, K.; Thurasamy, R. Questionnaire development and validity to measure sexual intention among youth in Malaysia. BMC Public Health 2017, 17, 157. [Google Scholar] [CrossRef] [Green Version]
  92. Abdul Kadir, A.; Mohd Arif, M.F.; Ishak, A.; Hassan, I.I.; Mohd Noor, N. Adaptation and Validation of the Malay Version of the Osteoarthritis Knee and Hip Quality of Life Questionnaire among Knee Osteoarthritis Patients. BioMed Res. Int. 2018, 2018, 4329751. [Google Scholar] [CrossRef] [Green Version]
  93. Achour, M.; Ab Halim, A.B.; Ali, A.R.H.; Sa’ari, C.Z.B.; Al-Nahari, A.A.A.Q. Religiosity and subjective well-being towards a balanced civilization: A study among Muslim older adults in Malaysia. J. Al-Tamaddun 2019, 14, 153–165. [Google Scholar] [CrossRef]
  94. Krauss, S.E.; Zeldin, S.; Abdullah, H.; Ortega, A.; Ali, Z.; Ismail, I.A.; Ariffin, Z. Malaysian youth associations as places for empowerment and engagement. Child. Youth Serv. Rev. 2020, 112, 104939. [Google Scholar] [CrossRef]
  95. Grothe, L.; Grothe, M.; Wingert, J.; Schomerus, G.; Speerforck, S. Stigma in Multiple Sclerosis: The Important Role of Sense of Coherence and Its Relation to Quality of Life. Int. J. Behav. Med. 2020, 1, 1–7. [Google Scholar] [CrossRef]
  96. Wong, F.Y.; Yang, L.; Yuen, J.W.; Chang, K.K.; Wong, F.K. Assessing quality of life using WHOQOL-BREF: A cross-sectional study on the association between quality of life and neighborhood environmental satisfaction, and the mediating effect of health-related behaviors. BMC Public Health 2018, 18, 1113. [Google Scholar] [CrossRef] [Green Version]
  97. Manaf, M.R.A.; Shaharuddin, M.A.-A.; Nawi, A.M.; Tauhid, N.M.; Othman, H.; Rahman, M.R.A.; Yusoff, H.M.; Safian, N.; Ng, P.Y.; Manaf, Z.A.; et al. Perceived Symptoms of Depression, Anxiety and Stress amongst Staff in a Malaysian Public University: A Workers Survey. Int. J. Environ. Res. Public Health 2021, 18, 11874. [Google Scholar] [CrossRef]
  98. Patrício, B.; Jesus, L.M.; Cruice, M.; Hall, A. Quality of life predictors and normative data. Soc. Indic. Res. 2014, 119, 1557–1570. [Google Scholar] [CrossRef] [Green Version]
  99. Jamaludin, T.S.S. Influence of Perceive Social Support or Pre-Operative Support Program on Quality of Life in Patients Awaiting Coronary Artery Bypass Grafting (CABG) Surgery: Mixed Method Systematic Review. Int. J. Care Sch. 2019, 2, 36–44. [Google Scholar]
  100. Abdul Basir, S.M.; Abdul Manaf, Z.; Ahmad, M.; Abdul Kadir, N.B.; Ismail, W.N.K.; Mat Ludin, A.F.; Shahar, S. Reliability and Validity of the Malay Mindful Eating Questionnaire (MEQ-M) among Overweight and Obese Adults. Int. J. Environ. Res. Public Health 2021, 18, 1021. [Google Scholar] [CrossRef]
  101. Abdul Kadir, N.B.; Rusyda, H.; Ismail, W.N.K.; Abdul Jalil, S.J.; Nurul-Azza, A.; Che Kasim, A.; Mohd Hoesni, S.; Mohd Rizal, A.M. Sustaining the Quality of Life for University Employees with Obesity Using Mindfulness Activities and Work Engagement: A Quasi-Experimental Design. Sustainability 2022, 14, 1925. [Google Scholar] [CrossRef]
Ijerph 19 07542 g001 550
Figure 1. Measurement model analysis.
Figure 1. Measurement model analysis.
Ijerph 19 07542 g001
Table
Table 1. Correlations.
Table 1. Correlations.
123456789101112131415161718192021222324
11
20.48 **1
3−0.19 **−0.15 *1
4−0.23 **−0.21 **0.71 **1
5−0.20 **−0.21 **0.54 **0.59 **1
6−0.26 **−0.16 *0.46 **0.58 **0.65 **1
7−0.25 **−0.130.33 **0.44 **0.40 **0.61 **1
8−0.26 **−0.22 **0.39 **0.44 **0.46 **0.52 **0.46 **1
9−0.16 *−0.030.19 **0.22 **0.22 **0.22 **0.20 **0.38 **1
10−0.26 **−0.110.32 **0.34 **0.33 **0.48 **0.36 **0.41 **0.45 **1
11−0.25 **−0.19 **0.40 **0.39 **0.43 **0.48 **0.36 **0.52 **0.44 **0.66 **1
12−0.15 *0.030.18 **0.20 **0.15 *0.24 **0.23 **0.41 **0.39 **0.32 **0.33 **1
13−0.31 **−0.30 **0.25 **0.26 **0.19 **0.34 **0.35 **0.45 **0.23 **0.26 **0.33 **0.39 **1
14−0.17 **−0.090.27 **0.25 **0.28 **0.27 **0.23 **0.36 **0.29 **0.35 **0.30 **0.33 **0.25 **1
15−0.40 **−0.20 **0.36 **0.35 **0.32 **0.41 **0.39 **0.60 **0.33 **0.40 **0.40 **0.42 **0.47 **0.58 **1
16−0.24 **−0.23 **0.36 **0.36 **0.39 **0.41 **0.37 **0.51 **0.22 **0.28 **0.35 **0.31 **0.36 **0.45 **0.66 **1
17−0.29 **−0.16 *0.40 **0.51 **0.43 **0.46 **0.39 **0.42 **0.48 **0.38 **0.41 **0.27 **0.33 **0.42 **0.57 **0.59 **1
18−0.23 **−0.18 **0.38 **0.43 **0.47 **0.43 **0.35 **0.40 **0.33 **0.36 **0.39 **0.18 **0.24 **0.43 **0.53 **0.52 **0.68 **1
19−0.07−0.060.14 *0.20 **0.25 **0.27 **0.24 **0.19 **0.19 **0.27 **0.18 **0.130.19 **0.23 **0.27 **0.32 **0.36 **0.59 **1
20−0.20 **−0.21 **0.37 **0.45 **0.46 **0.32 **0.34 **0.35 **0.31 **0.28 **0.35 **0.13 *0.27 **0.31 **0.41 **0.50 **0.56 **0.53 **0.36 **1
21−0.25 **−0.18 **0.28 **0.37 **0.40 **0.40 **0.35 **0.29 **0.21 **0.42 **0.36 **0.13 *0.26 **0.33 **0.35 **0.37 **0.45 **0.56 **0.44 **0.57 **1
22−0.13 *−0.18 **0.34 **0.34 **0.35 **0.33 **0.24 **0.32 **0.24 **0.28 **0.35 **0.110.26 **0.24 **0.25 **0.21 **0.44 **0.46 **0.31 **0.40 **0.51 **1
23−0.20 **−0.25 **0.30 **0.36 **0.31 **0.35 **0.25 **0.30 **0.15 *0.33 **0.31 **0.10.25 **0.25 **0.29 **0.35 **0.45 **0.40 **0.32 **0.41 **0.57 **0.58 **1
240.23 **0.26 **−0.23 **−0.22 **−0.23 **−0.30 **−0.21 **−0.21 **−0.16 *−0.20 **−0.17 *−0.11−0.24 **−0.33 **−0.29 **−0.27 **−0.38 **−0.36 **−0.19 **−0.21 **−0.26 **−0.21 **−0.16 *1
** Correlation is significant at the 0.01 level (two-tailed). * Correlation is significant at the 0.05 level (two-tailed). N = 228.
Table
Table 2. Factor loading and communalities.
Table 2. Factor loading and communalities.
FactorItem No.ItemFactor LoadingCommunality
3To what extent do you feel that physical pain prevents you from doing what you need to do?0.410.17
14How much do you need any medical treatment to function in your daily life?0.360.13
Physical
10Do you have enough energy for everyday life?0.580.33
15How well are you able to get around?0.400.16
16How satisfied are you with your sleep?0.420.21
17How satisfied are you with your ability to perform your daily living activities?0.690.47
18How satisfied are you with your capacity for work?0.610.38
5How much do you enjoy life?0.560.31
6To what extent do you feel your life to be meaningful?0.640.44
27How well are you able to concentrate?0.590.34
Psychological11Are you able to accept your bodily appearance?0.450.20
19How satisfied are you with yourself?0.680.47
26How often do you have negative feelings such as blue mood, despair, anxiety, depression?0.280.08
20How satisfied are you with your personal relationships?0.680.46
321How satisfied are you with sex life?0.420.18
Social22How satisfied are you with the support you get from your friends?0.530.28
8How safe do you feel in your daily life?0.660.44
9How healthy is your physical environment?0.500.25
412Have you enough money to meet your needs?
Environmental13How available to you is the information that you need in your day-to-day life?0.570.32
14To what extent do you have the opportunity for leisure activities?0.360.13
23How satisfied are you with the conditions of your living place?0.570.33
24How satisfied are you with your access to health services?0.480.23
25How satisfied are you with your transport?0.520.27
Table
Table 3. Convergent and discriminant validity.
Table 3. Convergent and discriminant validity.
FactorsCRAVEMSVMax R(H)AC
Physical0.750.530.100.970.71
Psychological0.900.600.150.940.77
Social0.830.620.070.970.79
Environmental0.780.550.140.830.74
N = 228. CR = composite reliability, AVE = average variance extracted, MSV = maximum shared variance, Max R (H) = maximum reliability, AC = Cronbach’s alpha.
Table
Table 4. Normality test findings.
Table 4. Normality test findings.
FactorItem No.ItemMSDSkewnessKurtosisRange
3To what extent do you feel that physical pain prevents you from doing what you need to do?2.770.850.010.171–5
1
Physical		4How much do you need any medical treatment to function in your daily life?2.180.900.33−0.311–5
10Do you have enough energy for everyday life?3.770.71−0.000.402–5
15How well are you able to get around?4.010.95−0.940.671–5
16How satisfied are you with your sleep?3.500.83−0.35−0.351–5
17How satisfied are you with your ability to perform your daily living activities?3.640.76−0.761.171–5
18How satisfied are you with your capacity for work?3.790.68−0.942.401–5
5How much do you enjoy life?3.810.640.10−0.452–5
6To what extent do you feel your life to be meaningful?4.070.690.17−0.612–5
27How well are you able to concentrate?3.700.630.13−0.402–5
Psychological11Are you able to accept your bodily appearance?3.381.11−0.00−0.571–5
19How satisfied are you with yourself?3.750.72−0.5110.121–5
26How often do you have negative feelings such as blue mood, despair, anxiety, depression?2.240.611.53.161–5
20How satisfied are you with your personal relationships?3.850.75−0.9620.181–5
321How satisfied are you with sex life?3.810.75−1.022.511–5
Social22How satisfied are you with the support you get from your friends?3.880.67−0.380.951–5
8How safe do you feel in your daily life?3.680.650.23−0.492–5
9How healthy is your physical environment?3.460.660.100.391–5
412Have you enough money to meet your needs?3.520.880.01−0.351–5
Environmental13How available to you is the information that you need in your day-to-day life?3.720.68−0.03−0.222–5
14To what extent do you have the opportunity for leisure activities?3.260.930.94−0.171–5
23How satisfied are you with the conditions of your living place?3.990.64−0.300.412–5
24How satisfied are you with your access to health services?3.960.66−0.421.171–5
25How satisfied are you with your transport?4.070.58−0.140.502–5
Table
Table 5. Cut-off point for model fit.
Table 5. Cut-off point for model fit.
MeasureThreshold Value
Chi-square/df (CMIN/df)<3 good
CFI>0.95 great; >0.90 acceptable
SRMR<0.09
RMSEA<0.05 good; 0.05–0.10 moderate; >0.10 bad
PCLOSE>0.05
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Share and Cite

MDPI and ACS Style

Rusyda, M.H.; Abdul Kadir, N.B.; Ismail, W.N.K.; Abdul Jalil, S.J.; Abdullah, N.-A.; Che Kasim, A.; Hoesni, S.M.; Abdul Manaf, M.R. Identifying the Psychometric Properties of the Malay Version of the WHOQOL-BREF among Employees with Obesity Problem. Int. J. Environ. Res. Public Health 2022, 19, 7542. https://doi.org/10.3390/ijerph19127542

AMA Style

Rusyda MH, Abdul Kadir NB, Ismail WNK, Abdul Jalil SJ, Abdullah N-A, Che Kasim A, Hoesni SM, Abdul Manaf MR. Identifying the Psychometric Properties of the Malay Version of the WHOQOL-BREF among Employees with Obesity Problem. International Journal of Environmental Research and Public Health. 2022; 19(12):7542. https://doi.org/10.3390/ijerph19127542

Chicago/Turabian Style

Rusyda, Mohd Helma, Nor Ba’yah Abdul Kadir, Wan Nur Khairunnisa Ismail, Siti Jamiaah Abdul Jalil, Nurul-Azza Abdullah, Arena Che Kasim, Suzana Mohd. Hoesni, and Mohd Rizal Abdul Manaf. 2022. "Identifying the Psychometric Properties of the Malay Version of the WHOQOL-BREF among Employees with Obesity Problem" International Journal of Environmental Research and Public Health 19, no. 12: 7542. https://doi.org/10.3390/ijerph19127542

APA Style

Rusyda, M. H., Abdul Kadir, N. B., Ismail, W. N. K., Abdul Jalil, S. J., Abdullah, N. -A., Che Kasim, A., Hoesni, S. M., & Abdul Manaf, M. R. (2022). Identifying the Psychometric Properties of the Malay Version of the WHOQOL-BREF among Employees with Obesity Problem. International Journal of Environmental Research and Public Health, 19(12), 7542. https://doi.org/10.3390/ijerph19127542

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop